If you’re a team leader, you’ve probably been thinking about AI since 2024. Some version of we need to get our people trained on AI has been in the back of your mind since before leadership started talking about it.
In 2025, you spent most of your time wrangling for the kind of budget that would actually make an impact, allowing you and your teams to bring in AI and Automation at scale, so you could keep up with the curve. This year, you’ve got the budget, you’re implementing tools and programs, and now it’s time to get everyone up to speed.
If this sounds familiar, you’re in the same shoes as 95% of the team leaders I’ve spoken to this year.
Before you ask your Learning Team to build the training you need, there are 5 questions you need to have answered before investing in training.
- What does “trained on AI” actually look like?
- Have you defined the role of AI — and the role of your people?
- Do your people have what they need to actually adopt it?
- Does your incentive structure actually reward employees?
- Are your people equipped for the work AI can’t do?
On the surface, all of these seem pretty basic. But after having dozens of conversations with learning leaders, COOs, and upper management in organizations that came to us for AI Learning Tools, I can guarantee you that not knowing the answers to these questions decreases your chances of successfully integrating AI and Automation in your ecosystems.
By a long shot. So let’s get into them.
What does “trained on AI” actually look like?
Most training requests are actually aimed at a feeling. Something like: we want our people comfortable with AI, or we want people to feel confident about our tools. It sounds like a goal, but it isn’t one you can build toward or measure — six months and a full program later, how would you prove your team is “comfortable”?
You’d be guessing, and so would they.
Instead, you need to think about what your people are doing differently. Before you scope a single module, get clear on:
- Six months from now, with the training done, what is your team actually producing, deciding, or handling that they couldn’t before?
- What is your strongest AI user doing that your average one isn’t — and can you describe it in plain, observable terms?
- Can you name “good” as something a person does, rather than something they feel?
That observable result is the thing most AI training skips. Picture a customer support team. “Trained on AI” might mean everyone can open the new assistant and generate a reply. But the version that actually moves the business is specific: a rep who drafts with AI, then edits for the customer’s real problem, knows the two or three cases that still need a human, and closes more tickets without a single frustrated follow-up. That is something you can teach toward and measure.
Key Takeaway: “Comfortable with the tool” is just hope with a budget attached.
I asked a VP recently what good looked like for just one role on his team. He paused, and then admitted he’d never actually defined it. That pause is the most important moment in the whole process. Until you can answer it, you don’t have a training problem to solve yet; you have a target to define. Name it first, and everything downstream gets easier.
Have you defined the role of AI — and the role of your people?
Most teams rolled out the tools but never redrew the roles. The work looks the same on paper, except now there’s an assistant sitting in the middle of it and nobody defined who does what. That gap is where confusion and fear grow. Before you train anyone, get clear on:
- In the actual work, where does the tool operate, and where does the person? Whose job is what now?
- What is the human specifically responsible for that AI is not?
- Do your employees know what their new role and expectations look like, or are they guessing?
When people don’t know their new role, they fill that gap with uncertainty. Some decide AI is coming for their job and resist it. Others trust the tools too much, and stop checking the work.
Take a marketing team that gets a content-generation tool and little guidance. One employee runs everything through it and ships first drafts as final editions. Another works tirelessly on original, human-written copy, and falls behind on volume. It’s the same team using the same tool, and the two failures look nothing alike. Neither one is a skills gap. It’s that no one drew the line where the writer’s judgment starts and the tool’s output ends.
Key Takeaway: You can’t train someone into a role you haven’t defined.
Redraw who owns what first, say it plainly, and the training finally has something real to build on.
Do your people have what they need to actually adopt it?
Giving someone access to a tool is not the same as giving them what it takes to use it well. Most companies rolled out the logins and assumed adoption would follow. It doesn’t, because the tool is only one piece. People also need workflows rebuilt around that tool and real time to learn it, and most got neither. Before you call it a training problem, check:
- Do they have workflows redesigned around the tools, or just the old process with AI wedged into it?
- Is there protected time to practice, learn, and adapt, or is that expected on top of a full workload?
- Who owns rebuilding the process, or did the tool arrive with no one responsible for fitting it into how the work actually gets done?
Adoption fails when AI is bolted onto a process that was already full.
Take a recruiting team handed an AI tool to screen and summarize applicants. It works well, but they’re still required to log every candidate by hand in the old system, get the same three sign-offs, and carry the same req load they had last quarter.
So the AI reads the resumes, and then the recruiter re-enters everything it found into fields nobody redesigned. The tool saved them twenty minutes, and the untouched process added back thirty. By the next hiring cycle, they’ve stopped opening it, because the shortcut was actually adding more hours to their already full schedules.
Key Takeaway: A tool dropped into an unchanged workflow just adds work.
Redesign the process around it and protect the time to learn it, and adoption stops being something you hope for and becomes something you built.
Does your incentive structure actually reward employees?
People do more of what gets rewarded and less of what gets punished. That sounds obvious, until you look at what actually happens to the employees who adopt AI first. They get faster, they clear their work early, and the reward is being handed more work.
The people who ignored the tools carry the same load they always did. Without meaning to, you’ve taught the whole team that adopting AI is how you earn a heavier workload for the same pay. Before you blame resistance on mindset, look at the math your people are running:
- When someone adopts AI and gets more efficient, are they rewarded for the output, or just handed the freed-up time as extra work?
- What happens to the people who don’t adopt it? Anything at all?
- Does using AI well make someone more valuable here, or easier to replace?
Employees read these signals fast, and they respond rationally.
Take a claims team where two adjusters go all in on an AI assistant and start clearing cases in half the time. Their manager sees the open capacity and routes them the overflow from everyone else. Same salary, double the caseload. The adjusters who never touched the tool keep moving at the old pace with no consequence.
Six months in, the two fastest have eased back to look “normal” again, and everyone else has all the proof they need that opting out was the smarter play. Nobody had a skills problem. They had a scoreboard that punished the exact behavior leadership said it wanted.
Key Takeaway: If adopting AI earns someone more work or a smaller role, resisting is the rational choice.
Fix the scoreboard first. Reward the output and protect the person who produced it, and you’ll find far fewer people were ever really resisting the tool.
Are your people equipped for the work AI can’t do?
If you’ve worked through the first four questions, you’ve cleared away most of what looks like a training problem but isn’t. What’s left is the part that genuinely lives in your people: the judgment AI can’t supply for them. This is the real training, and it’s a smaller, sharper thing than “teach everyone the tool.” It’s the skill to work alongside AI without handing over the thinking. Before you scope it, ask:
- Can your people tell which tasks belong to AI and which still need a human, or do they hand over everything and hope?
- Can they evaluate an AI output on its merits, instead of trusting it blindly or tossing it out of caution?
- Can they stand behind AI-assisted work and explain it to a manager, a client, or an auditor?
Where “good judgment” once meant having a head on your shoulders, it now means having the discernment to evaluate AI work.
Imagine two financial analysts using AI to build a quarterly forecast. AI returns a polished model in minutes, with clean charts and a confident summary. One analyst forwards it up the chain and beyond. The other interrogates it first: she checks which assumptions the model built in, catches that it’s still pricing on last year’s numbers, corrects it, and walks into the CFO’s office able to defend every figure and point to the two places she overrode the AI.
The model handed both analysts the same output. What it couldn’t hand either of them was the judgment to know how to use what was delivered. You need real people trained to analyze the work given to them, who can use discernment to make judgment calls, catch mistakes, and ask questions that weren’t considered.
Key Takeaway: Discernment is the one thing AI can’t hand your people.
AI does not substitute for a skilled person who can use their own brain to catch machine errors. The tool will only ever be as good as the leader vetting it. That person is who you should be investing in.
Your Next Steps
Looking back at these five questions, none of them are really about the training you intend to create itself. They’re about what surrounds it: whether you’ve defined what good looks like, whether the roles of AI and your people are clear, whether your workflows and incentives support the new way of working, and whether your people have the discernment AI can’t replace.
AI doesn’t fail because of the technology. It fails for one of two reasons:
- The way that work gets done wasn’t evaluated for AI and Automation readiness
- The people that do the work weren’t equipped with the tools, time, and skills required for a smooth AI and Automation transition.
We built the AI Readiness Assessment to work through these types of problems.
We look at both sides of your business, your people and the way the work actually gets done, and show you where AI will stall before you spend on it. You walk away knowing what to fix first, what to build, and where training will finally change performance.
So before you invest in getting everyone “trained up,” find out what’s actually standing between your organization and AI that works. Book a call, and we’ll take a look together.
